The acoustics of isolated vowels, e.g. of /a/, have in many studies been linked to pathological voice types, such as tracheoesophageal
(TE) voice. To study the possibilities of objective and automatic classification of pathological TE voice types, the acoustic
features of /a/ were quantified and subsequently classified using a suit of machine learning technologies. Best classification
was achieved by using a voiced-voiceless measurement and the harmonics-to-noise ratio. Other common acoustic features were
correlated to pathological type as well, but were less distinctive in classification. We conclude that for objective and automatic
classification of TE voice pathology, voicing distinction and harmonics-to-noise ratio are most relevant.

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